Population dynamics and regulation in the cave salamander <Emphasis Type="Italic">Speleomantes strinatii</Emphasis>

Time series analysis has been used to evaluate the mechanisms regulating population dynamics of mammals and insects, but has been rarely applied to amphibian populations. In this study, the influence of endogenous (density-dependent) and exogenous (density-independent) factors regulating population dynamics of the terrestrial plethodontid salamander Speleomantes strinatii was analysed by means of time series and multiple regression analyses. During the period 1993–2005, S. strinatii population abundance, estimated by a standardised temporary removal method, displayed relatively low fluctuations, and the autocorrelation function (ACF) analysis showed that the time series had a noncyclic structure. The partial rate correlation function (PRCF) indicated that a strong first-order negative feedback dominated the endogenous dynamics. Stepwise multiple regression analysis showed that the only climatic factor influencing population growth rate was the minimum winter temperature. Thus, at least during the study period, endogenous, density-dependent negative feedback was the main factor affecting the growth rate of the salamander population, whereas stochastic environmental variables, such as temperature and rainfall, seemed to play a minor role in regulation. These results stress the importance of considering both exogenous and endogenous factors when analysing amphibian long-term population dynamics.     

SBR工艺设计经验探讨

提出了SBR工艺中,反应池容积、反应时间以及需氧量的计算方法和设计经验。同时,在研读和对比国内外相关资料的基础上,探讨了SBR工艺发生污泥膨胀的可能性和原因,介绍了控制方法与措施。     

房地产开发项目环境影响评价的探讨

根据房地产开发项目的特点,阐述该项目施工期、运行期分别对环境产生的不良影响,着重分析施工期扬尘、运行期废水污染情况,以及减缓该不利影响的措施,最后总结房地产开发项目环境影响评价还须进一步探讨的问题。     

基于SVR的船舶溢油事故预测

船舶溢油事故的预测,是确定所需应急资源多少的重要依据;但船舶溢油事故具有小样本性,传统预测方法不再适用。针对船舶溢油事故的小样本性,该文选择SVR模型对其进行预测;SVR算法的关键是核函数的选择及自由参数的确定,针对上述问题该文分别采用交叉验证法、遗传算法;并在matlab软件上建立SVR模型,最后将该模型应用于上海港水域船舶溢油事故的预测。     

Application of a ‘bio-engineering’ technique to protect ghoramara island (Bay of Bengal) from severe erosion

Ghoramara Island is located ca. 150 km south of Kolkata, Bay of Bengal, India, in the Sundarban Delta complex. This sparsely populated island is suffering from severe coastal erosion and areal reduction for the last three decades, which results in the loss of major areas on the northwest coast. Both numerous households and a significant area of agricultural land and coastal stretches for fish drying have been lost. This has rendered thousands of people homeless as ‘environmental refugess’. In the present paper an attempt is made to study the erosion and accretion process through time series analysis using a GIS technique. Also, a study of remedial measures to protect the island using a ‘bio-engineering’ technique is reported in this paper. It has been shown that, in the absence of protection measures, the eastern shore will merge with the Indian mainland during the next 25 years, while the western part will be completely washed off.     

On the Estimation of Dispersal Kernels from Individual Mark-Recapture Data

We present a new method for estimating a distribution of dispersal displacements (a dispersal kernel) from mark-recapture data. One conventional method of calculating the dispersal kernel assumes that the distribution of displacements are Gaussian (e.g. resulting from a diffusion process) and that individuals remain within sampled areas. The first assumption prohibits an analysis of dispersal data that do not exhibit the Gaussian distribution (a common situation); the second assumption leads to underestimation of dispersal distance because individuals that disperse outside of sampling areas are never recaptured. Our method eliminates these two assumptions. In addition, the method can also accommodate mortality during a sampling period. This new method uses integrodifference equations to express the probability of spatial mark-recapture data; associated dispersal, survival, and recapture parameters are then estimated using a maximum likelihood method. We examined the accuracy of the estimators by applying the method to simulated data sets. Our method suggests designs for future mark-recapture experiments. Received: January 2004 / Revised: July 2005     

Kernel-based home range method for data with irregular sampling intervals

Studies of habitat selection and movements often use radio-tracking data for defining animal home ranges. Home ranges (HR) can be approximated by a utilization density distribution (UD) that instead of assuming uniform use of areas within HR boundary provides a probabilistic measure of animal space use. In reality, radio-tracking data contain periods of frequent autocorrelated observations interspersed with temporally more independent observations. Using such temporally irregular data directly may result in biased UD estimates, because areas that have been sampled intensively receive too much weight. The problem of autocorrelation has been tackled by resampling data with an appropriate time interval. However, resampling may cause a large reduction in the data set size along with a loss of information. Evidently, biased UD estimates or reduction in data may prejudice the results on animal habitat selection and movement. We introduce a new method for estimating UDs with temporally irregular data. The proposed method, called the time kernel, accounts for temporal aggregation of observations and gives less weight to temporally autocorrelated observations. A further extension of the method accounts also for spatially aggregated observations with relatively low weights given to observations that are both temporally and spatially aggregated. We test the behaviour of the time kernel method and its spatiotemporal version using simulated data. In addition, the method is applied to a data set of brown bear locations.     

Analysis of the global warming dynamics from temperature time series

Global warming is the observed increase of the average temperature of the Earth. The primary cause of this phenomenon is the release of the greenhouse gases by burning of fossil fuels, land cleaning, agriculture, among others, leading to the increase of the so-called greenhouse effect. An approach to deal with this important problem is the time series analysis. In this regard, different techniques can be applied to evaluate the global warming dynamics. This kind of analysis allows one to make better predictions increasing our comprehension of the phenomenon. This article applies nonlinear tools to analyze temperature time series establishing state space reconstruction and prediction. Since noise contamination is unavoidable in data acquisition, it is important to employ robust techniques. The method of delay coordinates is employed for state space reconstruction and delay parameters are evaluated using the method of average mutual information and the method of false nearest neighbors. Afterwards, the simple nonlinear prediction method is employed to estimate temperatures of the future. Temperature time series from different places of the planet are used. Initially, the approach is verified considering known parts of the time series and afterwards, results are extrapolated for future values estimating temperature until 2028. Results show that these techniques are interesting to estimate temperature time history, presenting coherent estimations.     

Assessment of population exposure to particulate matter pollution in Chongqing, China

To determine the population exposure to PM(10) in Chongqing, China, we developed an indirect model by combining information on the time activity patterns of various demographic subgroups with estimates of the PM(10) concentrations in different microenvironments (MEs). The spatial and temporal variations of the exposure to PM(10) were illustrated in a geographical information system (GIS). The population weighted exposure (PWE) for the entire population was 229, 155 and 211 microg/m(3), respectively, in winter, summer and as the annual average. Indoor PM(10) level at home was the largest contributor to the PWE, especially for the rural areas where high pollution levels were found due to solid fuels burning. Elder people had higher PM(10) exposure than adults and youth, due to more time spent in indoor MEs. The highest health risk due to particulate was found in the city zone and northeast regions, suggesting that pollution abatement should be prioritized in these areas.